Dynamic Data-Driven Downscaling to Quantify Extreme Rainfall and Flood Loss Risk

Anamitra Saha, Joaquín Salas, Sai Ravela

Abstract: The adverse socio-economic effects of natural hazards will likely worsen under climate change. Modeling their risk is essential to developing effective adaptation and mitigation strategies. However, climate models typically do not resolve the detailed information that risk quantification demands. Here, we propose a dynamic data-driven approach to estimate extreme rainfall-induced flood-loss risk. In this approach, coarse-resolution climate model outputs (\(0.25^{\circ } \times 0.25^{\circ }\)) are downscaled to high-resolution (\(0.01^{\circ } \times 0.01^{\circ }\)) rainfall. After that, rainfall, historical insurance loss provided by The Federal Emergency Management Agency (FEMA), and other geographic data train a flood-loss model. Our approach shows promise for quantifying flood-loss risk, showing a weighted average value of \(R^2 = 0.917\) for Cook County, Illinois, USA.